Fix errors and typos in the Estimators programmer's guide

diff --git a/tensorflow/docs_src/programmers_guide/estimators.md b/tensorflow/docs_src/programmers_guide/estimators.md
index ffadf29..de83011 100644 (file)
--- a/tensorflow/docs_src/programmers_guide/estimators.md
+++ b/tensorflow/docs_src/programmers_guide/estimators.md
@@ -21,18 +21,17 @@ Note: TensorFlow also includes a deprecated `Estimator` class at
 
 Estimators provide the following benefits:
 
-*   You can run Estimators-based models on a local host or on a
+*   You can run Estimator-based models on a local host or on a
     distributed multi-server environment without changing your model.
-    Furthermore, you can run Estimators-based models on CPUs, GPUs,
+    Furthermore, you can run Estimator-based models on CPUs, GPUs,
     or TPUs without recoding your model.
 *   Estimators simplify sharing implementations between model developers.
-*   You can develop a state of the art model with high-level intuitive code,
+*   You can develop a state of the art model with high-level intuitive code.
     In short, it is generally much easier to create models with Estimators
     than with the low-level TensorFlow APIs.
-*   Estimators are themselves built on tf.layers, which
+*   Estimators are themselves built on @{tf.layers}, which
     simplifies customization.
-*   Estimators build the graph for you.  In other words, you don't have to
-    build the graph.
+*   Estimators build the graph for you.
 *   Estimators provide a safe distributed training loop that controls how and
     when to:
     *   build the graph
@@ -57,7 +56,7 @@ the "plumbing" for you.  That is, pre-made Estimators create and manage
 pre-made Estimators let you experiment with different model architectures by
 making only minimal code changes.  @{tf.estimator.DNNClassifier$`DNNClassifier`},
 for example, is a pre-made Estimator class that trains classification models
-through dense, feed-forward neural networks.
+based on dense, feed-forward neural networks.
 
 
 ### Structure of a pre-made Estimators program
@@ -79,7 +78,7 @@ of the following four steps:
     an input function:
 
         def input_fn(dataset):
-           ...  # manipulate dataset, extracting feature names and the label
+           ...  # manipulate dataset, extracting the feature dict and the label
            return feature_dict, label
 
     (See @{$programmers_guide/datasets} for full details.)
@@ -96,13 +95,13 @@ of the following four steps:
         population = tf.feature_column.numeric_column('population')
         crime_rate = tf.feature_column.numeric_column('crime_rate')
         median_education = tf.feature_column.numeric_column('median_education',
-                            normalizer_fn='lambda x: x - global_education_mean')
+                            normalizer_fn=lambda x: x - global_education_mean)
 
 3.  **Instantiate the relevant pre-made Estimator.**  For example, here's
     a sample instantiation of a pre-made Estimator named `LinearClassifier`:
 
         # Instantiate an estimator, passing the feature columns.
-        estimator = tf.estimator.Estimator.LinearClassifier(
+        estimator = tf.estimator.LinearClassifier(
             feature_columns=[population, crime_rate, median_education],
             )